{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T09:43:16Z","timestamp":1784713396749,"version":"3.55.0"},"reference-count":66,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,21]],"date-time":"2022-01-21T00:00:00Z","timestamp":1642723200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>After lung cancer, breast cancer is the second leading cause of death in women. If breast cancer is detected early, mortality rates in women can be reduced. Because manual breast cancer diagnosis takes a long time, an automated system is required for early cancer detection. This paper proposes a new framework for breast cancer classification from ultrasound images that employs deep learning and the fusion of the best selected features. The proposed framework is divided into five major steps: (i) data augmentation is performed to increase the size of the original dataset for better learning of Convolutional Neural Network (CNN) models; (ii) a pre-trained DarkNet-53 model is considered and the output layer is modified based on the augmented dataset classes; (iii) the modified model is trained using transfer learning and features are extracted from the global average pooling layer; (iv) the best features are selected using two improved optimization algorithms known as reformed differential evaluation (RDE) and reformed gray wolf (RGW); and (v) the best selected features are fused using a new probability-based serial approach and classified using machine learning algorithms. The experiment was conducted on an augmented Breast Ultrasound Images (BUSI) dataset, and the best accuracy was 99.1%. When compared with recent techniques, the proposed framework outperforms them.<\/jats:p>","DOI":"10.3390\/s22030807","type":"journal-article","created":{"date-parts":[[2022,1,23]],"date-time":"2022-01-23T20:34:40Z","timestamp":1642970080000},"page":"807","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":283,"title":["Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion"],"prefix":"10.3390","volume":"22","author":[{"given":"Kiran","family":"Jabeen","sequence":"first","affiliation":[{"name":"Department of Computer Science, HITEC University Taxila, Taxila 47080, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6763-2123","authenticated-orcid":false,"given":"Muhammad Attique","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, HITEC University Taxila, Taxila 47080, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Majed","family":"Alhaisoni","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, University of Ha\u2019il, Ha\u2019il 55211, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7672-1187","authenticated-orcid":false,"given":"Usman","family":"Tariq","sequence":"additional","affiliation":[{"name":"College of Computer Engineering and Science, Prince Sattam Bin Abdulaziz University, Al-Kharaj 11942, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4870-1493","authenticated-orcid":false,"given":"Yu-Dong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Leicester, Leicester LE1 7RH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ameer","family":"Hamza","sequence":"additional","affiliation":[{"name":"Department of Computer Science, HITEC University Taxila, Taxila 47080, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Art\u016bras","family":"Mickus","sequence":"additional","affiliation":[{"name":"Department of Applied Informatics, Vytautas Magnus University, LT-44404 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9990-1084","authenticated-orcid":false,"given":"Robertas","family":"Dama\u0161evi\u010dius","sequence":"additional","affiliation":[{"name":"Department of Applied Informatics, Vytautas Magnus University, LT-44404 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yu, K., Chen, S., and Chen, Y. (2021). Tumor Segmentation in Breast Ultrasound Image by Means of Res Path Combined with Dense Connection Neural Network. Diagnostics, 11.","DOI":"10.3390\/diagnostics11091565"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.gendis.2018.05.001","article-title":"Breast cancer development and progression: Risk factors, cancer stem cells, signaling pathways, genomics, and molecular pathogenesis","volume":"5","author":"Feng","year":"2018","journal-title":"Genes Dis."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Badawy, S.M., Mohamed, A.E.-N.A., Hefnawy, A.A., Zidan, H.E., GadAllah, M.T., and El-Banby, G.M. (2021). Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning\u2014A feasibility study. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0251899"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6175","DOI":"10.7150\/jca.35901","article-title":"Clinical implications of tumor-infiltrating immune cells in breast cancer","volume":"10","author":"Zhang","year":"2019","journal-title":"J. Cancer"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Irfan, R., Almazroi, A.A., Rauf, H.T., Dama\u0161evi\u010dius, R., Nasr, E., and Abdelgawad, A. (2021). Dilated Semantic Segmentation for Breast Ultrasonic Lesion Detection Using Parallel Feature Fusion. Diagnostics, 11.","DOI":"10.3390\/diagnostics11071212"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.bbe.2018.01.001","article-title":"Comparative assessment of texture features for the identification of cancer in ultrasound images: A review","volume":"38","author":"Faust","year":"2018","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Pourasad, Y., Zarouri, E., Salemizadeh Parizi, M., and Salih Mohammed, A. (2021). Presentation of Novel Architecture for Diagnosis and Identifying Breast Cancer Location Based on Ultrasound Images Using Machine Learning. Diagnostics, 11.","DOI":"10.3390\/diagnostics11101870"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1136\/bmj.321.7263.745","article-title":"Breast cancer","volume":"321","author":"Sainsbury","year":"2000","journal-title":"BMJ"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"53","DOI":"10.3389\/fonc.2020.00053","article-title":"Deep learning vs. radiomics for predicting axillary lymph node metastasis of breast cancer using ultrasound images: Don\u2019t forget the peritumoral region","volume":"10","author":"Sun","year":"2020","journal-title":"Front. Oncol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Almajalid, R., Shan, J., Du, Y., and Zhang, M. (2018, January 17\u201320). Development of a deep-learning-based method for breast ultrasound image segmentation. Proceedings of the 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, USA.","DOI":"10.1109\/ICMLA.2018.00179"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ouahabi, A. (2012). Signal and Image Multiresolution Analysis, John Wiley & Sons.","DOI":"10.1002\/9781118568767"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3461","DOI":"10.3390\/e17053461","article-title":"Nonparametric denoising methods based on contourlet transform with sharp frequency localization: Application to low exposure time electron microscopy images","volume":"17","author":"Ahmed","year":"2015","journal-title":"Entropy"},{"key":"ref_13","first-page":"1","article-title":"Ultrasound for breast cancer detection globally: A systematic review and meta-analysis","volume":"5","author":"Sood","year":"2019","journal-title":"J. Glob. Oncol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"102828","DOI":"10.1016\/j.bspc.2021.102828","article-title":"Breast mass classification with transfer learning based on scaling of deep representations","volume":"69","author":"Byra","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/S0929-6441(08)60005-3","article-title":"Computer-aided diagnosis in breast ultrasound","volume":"16","author":"Chen","year":"2008","journal-title":"J. Med. Ultrasound"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Moustafa, A.F., Cary, T.W., Sultan, L.R., Schultz, S.M., Conant, E.F., Venkatesh, S.S., and Sehgal, C.M. (2020). Color doppler ultrasound improves machine learning diagnosis of breast cancer. Diagnostics, 10.","DOI":"10.3390\/diagnostics10090631"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"928","DOI":"10.1016\/j.acra.2007.04.016","article-title":"Breast ultrasound computer-aided diagnosis using BI-RADS features","volume":"14","author":"Shen","year":"2007","journal-title":"Acad. Radiol."},{"key":"ref_18","unstructured":"Lee, J.-H., Seong, Y.K., Chang, C.-H., Park, J., Park, M., Woo, K.-G., and Ko, E.Y. (September, January 28). Fourier-based shape feature extraction technique for computer-aided b-mode ultrasound diagnosis of breast tumor. Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, USA."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1007\/s10278-012-9499-x","article-title":"Breast ultrasound image classification based on multiple-instance learning","volume":"25","author":"Ding","year":"2012","journal-title":"J. Digit. Imaging"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"7894705","DOI":"10.1155\/2017\/7894705","article-title":"Sparse representation based multi-instance learning for breast ultrasound image classification","volume":"2017","author":"Bing","year":"2017","journal-title":"Comput. Math. Methods Med."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Prabhakar, T., and Poonguzhali, S. (September, January 31). Automatic detection and classification of benign and malignant lesions in breast ultrasound images using texture morphological and fractal features. Proceedings of the 2017 10th Biomedical Engineering International Conference (BMEiCON), Hokkaido, Japan.","DOI":"10.1109\/BMEiCON.2017.8229114"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.ejrad.2017.07.027","article-title":"Dual-modal computer-assisted evaluation of axillary lymph node metastasis in breast cancer patients on both real-time elastography and B-mode ultrasound","volume":"95","author":"Zhang","year":"2017","journal-title":"Eur. J. Radiol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"300","DOI":"10.2214\/AJR.18.20392","article-title":"New frontiers: An update on computer-aided diagnosis for breast imaging in the age of artificial intelligence","volume":"212","author":"Gao","year":"2019","journal-title":"Am. J. Roentgenol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1148\/radiol.2019182627","article-title":"Artificial intelligence for mammography and digital breast tomosynthesis: Current concepts and future perspectives","volume":"293","author":"Geras","year":"2019","journal-title":"Radiology"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Fujioka, T., Mori, M., Kubota, K., Oyama, J., Yamaga, E., Yashima, Y., Katsuta, L., Nomura, K., Nara, M., and Oda, G. (2020). The utility of deep learning in breast ultrasonic imaging: A review. Diagnostics, 10.","DOI":"10.3390\/diagnostics10121055"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.2174\/1573405616666200406110547","article-title":"Breast cancer detection and classification using traditional computer vision techniques: A comprehensive review","volume":"16","author":"Zahoor","year":"2020","journal-title":"Curr. Med. Imaging"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kadry, S., Rajinikanth, V., Taniar, D., Dama\u0161evi\u010dius, R., and Valencia, X.P.B. (2021). Automated segmentation of leukocyte from hematological images\u2014A study using various CNN schemes. J. Supercomput., 1\u201321.","DOI":"10.1007\/s11227-021-04125-4"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2600","DOI":"10.3906\/elk-2101-133","article-title":"Malignant skin melanoma detection using image augmentation by oversampling in nonlinear lower-dimensional embedding manifold","volume":"29","author":"Misra","year":"2021","journal-title":"Turk. J. Electr. Eng. Comput. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Maqsood, S., Dama\u0161evi\u010dius, R., and Maskeli\u016bnas, R. (2021). Hemorrhage detection based on 3d cnn deep learning framework and feature fusion for evaluating retinal abnormality in diabetic patients. Sensors, 21.","DOI":"10.3390\/s21113865"},{"key":"ref_30","first-page":"34","article-title":"Intelligent Deep Learning and Improved Whale Optimization Algorithm Based Framework for Object Recognition","volume":"11","author":"Hussain","year":"2021","journal-title":"Hum. Cent. Comput. Inf. Sci."},{"key":"ref_31","unstructured":"Kadry, S., Parwekar, P., Dama\u0161evi\u010dius, R., Mehmood, A., Khan, J.A., Naqvi, S.R., and Khan, M.A. (2021). Human gait analysis for osteoarthritis prediction: A framework of deep learning and kernel extreme learning machine. Complex Intell. Syst., 1\u201319."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Dhungel, N., Carneiro, G., and Bradley, A.P. (2016). The automated learning of deep features for breast mass classification from mammograms. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Athens, Greece, 17\u201321 October 2016, Springer.","DOI":"10.1007\/978-3-319-46723-8_13"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"7286","DOI":"10.3390\/s21217286","article-title":"COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion","volume":"21","author":"Alhaisoni","year":"2021","journal-title":"Sensors"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Odusami, M., Maskeli\u016bnas, R., Dama\u0161evi\u010dius, R., and Krilavi\u010dius, T. (2021). Analysis of features of alzheimer\u2019s disease: Detection of early stage from functional brain changes in magnetic resonance images using a finetuned resnet18 network. Diagnostics, 11.","DOI":"10.3390\/diagnostics11061071"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Nawaz, M., Nazir, T., Masood, M., Mehmood, A., Mahum, R., Kadry, S., and Thinnukool, O. (2021). Analysis of Brain MRI Images Using Improved CornerNet Approach. Diagnostics, 11.","DOI":"10.3390\/diagnostics11101856"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Farzaneh, N., Williamson, C.A., Jiang, C., Srinivasan, A., Bapuraj, J.R., Gryak, J., Najarian, K., and Soroushmehr, S. (2020). Automated segmentation and severity analysis of subdural hematoma for patients with traumatic brain injuries. Diagnostics, 10.","DOI":"10.3390\/diagnostics10100773"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Meng, L., Zhang, Q., and Bu, S. (2021). Two-Stage Liver and Tumor Segmentation Algorithm Based on Convolutional Neural Network. Diagnostics, 11.","DOI":"10.3390\/diagnostics11101806"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"20704","DOI":"10.1109\/JSEN.2021.3100151","article-title":"Ear recognition based on deep unsupervised active learning","volume":"21","author":"Khaldi","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_39","first-page":"319","article-title":"COVID19 classification using CT images via ensembles of deep learning models","volume":"69","author":"Majid","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Sharif, M.I., Alhussein, M., Aurangzeb, K., and Raza, M. (2021). A decision support system for multimodal brain tumor classification using deep learning. Complex Intell. Syst., 1\u201314.","DOI":"10.1007\/s40747-021-00321-0"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"022052","DOI":"10.1088\/1742-6596\/1345\/2\/022052","article-title":"Fusion of handcrafted and deep features for medical image classification","volume":"1345","author":"Liu","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"104879","DOI":"10.1016\/j.compbiomed.2021.104879","article-title":"3D shearlet-based descriptors combined with deep features for the classification of Alzheimer\u2019s disease based on MRI data","volume":"138","author":"Alinsaif","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4267","DOI":"10.1109\/JBHI.2021.3067789","article-title":"Multi-Class Skin Lesion Detection and Classification via Teledermatology","volume":"25","author":"Khan","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Masud, M., Rashed, A.E.E., and Hossain, M.S. (2020). Convolutional neural network-based models for diagnosis of breast cancer. Neural Comput. Appl., 1\u201312.","DOI":"10.1007\/s00521-020-05394-5"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Jim\u00e9nez-Gaona, Y., Rodr\u00edguez-\u00c1lvarez, M.J., and Lakshminarayanan, V. (2020). Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review. Appl. Sci., 10.","DOI":"10.3390\/app10228298"},{"key":"ref_46","first-page":"78","article-title":"A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images","volume":"1","author":"Zeebaree","year":"2020","journal-title":"J. Appl. Sci. Technol. Trends"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Huang, K., Zhang, Y., Cheng, H., and Xing, P. (2021, January 5\u20139). Shape-adaptive convolutional operator for breast ultrasound image segmentation. Proceedings of the 2021 IEEE International Conference on Multimedia and Expo (ICME), Shenzhen, China.","DOI":"10.1109\/ICME51207.2021.9428287"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Sadad, T., Hussain, A., Munir, A., Habib, M., Ali Khan, S., Hussain, S., Yang, S., and Alawairdhi, M. (2020). Identification of breast malignancy by marker-controlled watershed transformation and hybrid feature set for healthcare. Appl. Sci., 10.","DOI":"10.3390\/app10061900"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"e12713","DOI":"10.1111\/exsy.12713","article-title":"Breast ultrasound tumour classification: A Machine Learning\u2014Radiomics based approach","volume":"38","author":"Mishra","year":"2021","journal-title":"Expert Syst."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"189343","DOI":"10.1109\/ACCESS.2020.3029684","article-title":"Contextual level-set method for breast tumor segmentation","volume":"8","author":"Hussain","year":"2020","journal-title":"IEEE Access"},{"key":"ref_51","unstructured":"Xiangmin, H., Jun, W., Weijun, Z., Cai, C., Shihui, Y., and Jun, S. (2020). Deep Doubly Supervised Transfer Network for Diagnosis of Breast Cancer with Imbalanced Ultrasound Imaging Modalities. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"105361","DOI":"10.1016\/j.cmpb.2020.105361","article-title":"Computer-aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks","volume":"190","author":"Moon","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"102027","DOI":"10.1016\/j.bspc.2020.102027","article-title":"Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network","volume":"61","author":"Byra","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Kadry, S., Dama\u0161evi\u010dius, R., Taniar, D., Rajinikanth, V., and Lawal, I.A. (2021, January 25\u201327). Extraction of tumour in breast MRI using joint thresholding and segmentation\u2013A study. Proceedings of the 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII), Chennai, India.","DOI":"10.1109\/ICBSII51839.2021.9445152"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Lahoura, V., Singh, H., Aggarwal, A., Sharma, B., Mohammed, M., Dama\u0161evi\u010dius, R., Kadry, S., and Cengiz, K. (2021). Cloud computing-based framework for breast cancer diagnosis using extreme learning machine. Diagnostics, 11.","DOI":"10.3390\/diagnostics11020241"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Maqsood, S., Damasevicius, R., and Shah, F.M. (2021). An Efficient Approach for the Detection of Brain Tumor Using Fuzzy Logic and U-NET CNN Classification, International Conference on Computational Science and Its Applications, Springer.","DOI":"10.1007\/978-3-030-86976-2_8"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Rajinikanth, V., Kadry, S., Taniar, D., Damasevicius, R., and Rauf, H.T. (2021, January 26\u201327). Breast-cancer detection using thermal images with marine-predators-algorithm selected features. Proceedings of the 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII), Noida, India.","DOI":"10.1109\/ICBSII51839.2021.9445166"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.patrec.2021.01.010","article-title":"Deep learning for real-time semantic segmentation: Application in ultrasound imaging","volume":"144","author":"Ouahabi","year":"2021","journal-title":"Pattern Recognit. Lett."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"104863","DOI":"10.1016\/j.dib.2019.104863","article-title":"Dataset of breast ultrasound images","volume":"28","author":"Gomaa","year":"2020","journal-title":"Data Brief"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"106960","DOI":"10.1016\/j.compeleceng.2020.106960","article-title":"Prediction of COVID-19-pneumonia based on selected deep features and one class kernel extreme learning machine","volume":"90","author":"Khan","year":"2021","journal-title":"Comput. Electr. Eng."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Khan, M.A., Sharif, M.I., Raza, M., Anjum, A., Saba, T., and Shad, S.A. (2019). Skin lesion segmentation and classification: A unified framework of deep neural network features fusion and selection. Expert Syst., e12497.","DOI":"10.1111\/exsy.12497"},{"key":"ref_62","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.1002\/mp.13966","article-title":"Breast tumor classification through learning from noisy labeled ultrasound images","volume":"47","author":"Cao","year":"2020","journal-title":"Med. Phys."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1016\/j.bbe.2021.05.007","article-title":"A method for segmentation of tumors in breast ultrasound images using the variant enhanced deep learning","volume":"41","author":"Ilesanmi","year":"2021","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"106221","DOI":"10.1016\/j.cmpb.2021.106221","article-title":"Breast ultrasound tumor image classification using image decomposition and fusion based on adaptive multi-model spatial feature fusion","volume":"208","author":"Zhuang","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"106018","DOI":"10.1016\/j.cmpb.2021.106018","article-title":"Semi-supervised GAN-based Radiomics Model for Data Augmentation in Breast Ultrasound Mass Classification","volume":"203","author":"Pang","year":"2021","journal-title":"Comput. 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